Introduction to stream processing with Apache Flink

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© 2017 MapR Technologies @tgrall {“about” : “me”} Tugdual “Tug” Grall • MapR : Technical Evangelist • MongoDB, Couchbase, eXo, Oracle • NantesJUG co-founder  • @tgrall • http://tgrall.github.io • [email protected] / [email protected]

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© 2017 MapR Technologies @tgrall 3 Open Source Engines & Tools Commercial Engines & Applications Utility-Grade Platform Services Data Processing Web-Scale Storage MapR-FS MapR-DB Search and Others Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace High Availability MapR Streams Cloud and Managed Services Search and Others Unified Management and Monitoring Search and Others Event Streaming Database Custom Apps MapR Converged Data Platform

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© 2017 MapR Technologies @tgrall Streaming and Batch 2016-3-1  12:00 am 2016-3-1  1:00 am 2016-3-1  2:00 am 2016-3-11  11:00pm 2016-3-12  12:00am 2016-3-12  1:00am 2016-3-11  10:00pm 2016-3-12  2:00am 2016-3-12  3:00am … partition partition

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© 2017 MapR Technologies @tgrall Flink Architecture Deployment Local Cluster Cloud Single JVM Standalone, YARN, Mesos AWS, Google Core Runtime Distributed Streaming Dataﬂow DataSet API Batch Processing API & Libraries FlinkML Machine Learning Gelly Graph Processing Table Relational

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© 2017 MapR Technologies @tgrall Flink Architecture Deployment Local Cluster Cloud Single JVM Standalone, YARN, Mesos AWS, Google Core Runtime Distributed Streaming Dataﬂow DataSet API Batch Processing DataStream API Stream Processing API & Libraries FlinkML Machine Learning Gelly Graph Processing Table Relational

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© 2017 MapR Technologies @tgrall Flink Architecture Deployment Local Cluster Cloud Single JVM Standalone, YARN, Mesos AWS, Google Core Runtime Distributed Streaming Dataﬂow DataSet API Batch Processing DataStream API Stream Processing API & Libraries FlinkML Machine Learning Gelly Graph Processing Table Relational CEP Event Processing Table Relational

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© 2017 MapR Technologies @tgrall Batch & Stream case class Word (word: String, frequency: Int) // DataSet API - Batch val lines: DataSet[String] = env.readTextFile(…) lines.flatMap {line => line.split(“ ”).map(word => Word(word,1))} .groupBy("word").sum("frequency") .print() // DataStream API - Streaming val lines: DataSream[String] = env.fromSocketStream(...) lines.flatMap {line => line.split(“ ”).map(word => Word(word,1))} .keyBy("word”).window(Time.of(5,SECONDS)) .every(Time.of(1,SECONDS)).sum(”frequency") .print()

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© 2017 MapR Technologies @tgrall Flink Ecosystem Source Sink Apache Kafka MapR Streams AWS Kinesis RabbitMQ Twitter Apache Bahir … Apache Kafka MapR Streams AWS Kinesis RabbitMQ Elasticsearch HDFS/MapR-FS …

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© 2017 MapR Technologies @tgrall 10 Billion events/day 2Tb of data/day 30 Applications 2Pb of storage and growing Source Bouyges Telecom : http://berlin.ﬂink-forward.org/wp-content/uploads/2016/07/Thomas-Lamirault_Mohamed-Amine-Abdessemed-A-brief-history-of-time-with-Apache-Flink.pdf

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© 2017 MapR Technologies @tgrall What Is Event-Time Processing 1977 1980 1983 1999 2002 2005 2015 Processing Time Episode  IV Episode  V Episode  VI Episode  I Episode  II Episode  III Episode  VII Event Time

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© 2017 MapR Technologies @tgrall Complex Event Processing • Analyzing a stream of events and drawing conclusions • “if A and then B ! infer event C” • Demanding requirements on stream processor • Low latency! • Exactly-once semantics & event-time support

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© 2017 MapR Technologies @tgrall Order Events Process is reflected in a stream of order events Order(orderId, tStamp, “received”) Shipment(orderId, tStamp, “shipped”) Delivery(orderId, tStamp, “delivered”) orderId: Identifies the order tStamp: Time at which the event happened

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© 2017 MapR Technologies @tgrall CEP to the Rescue Define processing and delivery intervals (SLAs) ProcessSucc(orderId, tStamp, duration) ProcessWarn(orderId, tStamp) DeliverySucc(orderId, tStamp, duration) DeliveryWarn(orderId, tStamp) orderId: Identifies the order tStamp: Time when the event happened duration: Duration of the processing/delivery

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© 2017 MapR Technologies @tgrall Processing: Order ! Shipment val processingPattern = Pattern .begin[Event]("received").subtype(classOf[Order]) .followedBy("shipped").where(_.status == "shipped") .within(Time.hours(1))

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© 2017 MapR Technologies @tgrall val processingPattern = Pattern .begin[Event]("received").subtype(classOf[Order]) .followedBy("shipped").where(_.status == "shipped") .within(Time.hours(1)) val processingPatternStream = CEP.pattern( input.keyBy("orderId"), processingPattern) val procResult: DataStream[Either[ProcessWarn, ProcessSucc]] = processingPatternStream.select { (pP, timestamp) => // Timeout handler ProcessWarn(pP("received").orderId, timestamp) } { fP => // Select function ProcessSucc( fP("received").orderId, fP("shipped").tStamp, fP("shipped").tStamp – fP("received").tStamp) } Processing: Order ! Shipment

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© 2017 MapR Technologies @tgrall Demonstration • https://github.com/mapr-demos/mapr-streams-flink-demo • https://github.com/mapr-demos/wifi-sensor-demo • http://tgrall.github.io/blog/2016/10/12/getting-started-with- apache-flink-and-kafka/ • http://tgrall.github.io/blog/2016/10/17/getting-started-with- apache-flink-and-mapr-streams/ • more soon….